In his presentation, Dr. Mahani discussed a strategy for accelerating MCMC sampling, using GPUs and Gibbs Sampling, with applications in estimating Hierarchical Bayesian models. Typically the MCMC estimation is both time consuming and difficult to parallelize due to its sequential nature. In order to accelerate Gibbs sampling, Dr. Mahani defined a taxonomy of variables and then provided GPU parallelization strategies for each variable type and their GPU implementation. When used for large problems, such parallelization approaches can accelerate estimation 30 to 100 times. This provides the benefit of lowering computation cost, and also makes the modeling experience more interactive.

"Modeling should be a dynamic process,” explained Mahani. “The GPU parallelization of Gibbs sampling enables our modeling team to significantly shorten the modeling lifecycle and maintain intellectual focus on the task at hand which translates into higher-quality analytics in a much shorter time frame for our clients."

To view a video of the “GPU Parallelization of Gibbs Sampling” presentation please visit the following link: http://sentrana.wistia.com/m/B676V6